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Author:

Miao, Yinghao (Miao, Yinghao.) | Chen, Guanghui (Chen, Guanghui.) | Wang, Wentao (Wang, Wentao.) | Gong, Xiuqing (Gong, Xiuqing.)

Indexed by:

EI Scopus

Abstract:

This paper presents a method to characterize asphalt pavement macrotexture using the gray-tone difference matrix (GTDM) and discusses the potentials of the GTDM indicators for skid resistance evaluation. There are 37 field sites included in the data collection, which cover 6 types of asphalt pavement surfaces. The mean profile depth derived from 3-D macrotexture measurements (MPD3) has a significant relationship with the mean texture depth (MTD), which can be described by a logarithm model with R2 of 0.962. There is no significant linear relationship between the friction coefficient at a speed of 60 km/h (DFT60) and macrotexture indicators. A nonlinear model with British pendulum number (BPN) incorporated can relate DFT60 to MTD or indicator fcon. A comparison with MTD shows that GTDM-based fcon has a potential to be a macrotexture indicator for skid resistance evaluation, which describes the general height difference and the average local height difference of pavement macrotexture. A relatively high fcon is helpful for improving asphalt pavement skid resistance. ©, 2015, Southeast University. All right reserved.

Keyword:

Skid resistance Textures Asphalt pavements Matrix algebra Friction Asphalt

Author Community:

  • [ 1 ] [Miao, Yinghao]Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing; 100124, China
  • [ 2 ] [Chen, Guanghui]Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing; 100124, China
  • [ 3 ] [Wang, Wentao]Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing; 100124, China
  • [ 4 ] [Gong, Xiuqing]Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing; 100124, China

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Source :

Journal of Southeast University (English Edition)

ISSN: 1003-7985

Year: 2015

Issue: 3

Volume: 31

Page: 389-395

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 8

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 14

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